Optical character recognition techniques have been somewhat successful in recognizing printed text in standard typeset fonts. If the printed data is in fact recognized, it may be converted to an electronic format, in which case it may be rapidly retrieved, distributed, and manipulated.
Recognizing hand-drawn characters is much more difficult than recognizing standard printed data. Recognition of symbols, such as those used in engineering drawings, is even more difficult. While the present invention focuses on the particularly difficult problem of character and symbol recognition in engineering drawings, the method and apparatus may also be successfully applied to less rigorous problems.
It is estimated that there are over three billion engineering drawings in the United States and Canada, and that over 25 million new engineering drawings are produced each year. A number of problems exist in relation to these drawings. First, it is extremely expensive to file, copy, and prepare them. In addition, the drawings are commonly misplaced or otherwise missing. Another problem relates to the fact that in many instances multiple copies of a drawing exist. As a result, efforts must be made to insure that a single master copy contains all modifications. Modifying existing drawings is an expensive and time-consuming process which commonly introduces errors.
The nuclear power industry provides a specific example of the magnitude and importance of the drawing management problem. A large nuclear power plant may have several hundred thousand D (24" by 36") and E (36" by 48") sized drawings which depict piping, instrumentation, electrical connections, and other information describing the plant and its subsystems.
Regulations from the Federal Nuclear Regulatory Commission require that the plant operators have rapid access to the information on these drawings. Safety considerations require that plant drawings be readily accessible. In addition, during maintenance and retrofit projects, access to the drawings is vital.
Currently, it takes several hours to locate and copy a particular drawing. Moreover, it can take several days to retrieve a particular drawing and transport it to a given field location. Even if one obtains a desired drawing, the drawing may reference other drawings, in which case the time consuming cycle of obtaining a drawing is repeated. In sum, the cost of information gathering may be a substantial portion of the total project cost.
To avoid these problems, efforts have been made to convert engineering drawings to an electronic format. If the engineering drawings could be placed in an electronic format, a number of benefits would result. For instance, the cost of revising the drawings would decrease. The time needed to modify a drawing on a Computer Aided Drafting (CAD) system is typically a fraction of the time need to accomplish the same revision using paper and pencil techniques. Another benefit is that CAD drawings have increased accuracy.
Currently, CAD drawings are not susceptible to qualitative and quantitative modeling. Thus, stress analyses and safety analyses are not easily performed in conjunction with CAD drawings.
With an integrated database, equipment drawings may be linked to pictorial databases to provide a clear facsimile of equipment. This would be especially advantageous if the equipment was otherwise difficult to view because it was remotely located or located in a harsh environment.
Electronic storage of the drawings would result in improved control and access of the drawings. If the drawings are in an electronic format, electronic transmission and distribution may be exploited.
These benefits are not presently available because it is very difficult to convert engineering drawings into useful electronic format. One approach to converting drawings into an electronic format is to manually enter drawings into a CAD system. Naturally, this is time-consuming, error-prone, and expensive. Another approach is to initially scan the drawing and then have a draftsman trace the figure on a CAD workstation. However, present scanning techniques fail to identify characters and symbols or actually misidentify them. In the first case, the characters and symbols must be subsequently identified by an individual. In the latter case, the misidentification could lead to subsequent problems. Thus, there are a number of problems associated with the scanning technique. Consequently, the scanned image is not particularly advantageous since it is still time consuming to edit and revise the resultant document.
Present character and symbol recognition techniques for interpreting scanned images rely upon artificial intelligence (AI) methods. While each separate method has been somewhat successful, these methods still fail to recognize a substantial percentage of the characters and symbols they process. As a result, a large amount of time is required to complete the electronic figure and to eliminate the errors introduced during the scanning process. Consequently, the effectiveness of utilizing one AI technique alone is marginal.
A relatively new AI technology, neural networks, has displayed a capability for recognizing hand-drawn characters. Neural networks utilized in an image processing capacity are disclosed in U.S. Pat. Nos. 4,941,122 and 4,876,731, these patents are expressly incorporated by reference herein.
Neural networks emulate the ability of the human brain to identify patterns by simulating the operation of human nerve cells, called neurons. Artificial neural systems are networks of interconnected processing elements, or neurons. Each neuron can have multiple input signals, but generates only one output signal.
Neural networks typically include a number of layers. The first layer receives the input data and conveys it as input to the next layer. Each layer includes a group of neurons operating in parallel on the input signals fed to the layer. The neurons in each group of neurons in a layer act as feature detectors.
The feature detector function is realized through multiplying the input signal by a plurality of weight factors. The product is then summed by a summing network which applies its output through a function to generate the output of the neuron. The function of each neuron is therefore dependent upon the input applied to the neuron and the neuron's associated weighing.
The weighing of a neuron may be calculated so as to render the neuron sensitive to lines in a horizontal direction of an image. In contrast, the weighing may be such that the neuron is sensitive to lines in a vertical direction. In any case, a layer may have many groups of neurons which are processing different characteristics in parallel. This neuron behavior is generally governed by first-order ordinary differential equations in the output signal variable.
By adjusting the weighing associated with the neurons, the network can become adaptive. That is, by readjusting the neurons in such a way that they generate a desired output for a given input, they can be used to provide an identifying output signal based upon the unknown input signal they receive. Thus, different patterns may be recognized by adapting the neural network to perform different logic functions and thereby respond to significant features which characterize a given pattern.
In order to make neural networks a feasible tool in recognition of items in hand-written drawings, a number of constraints must be overcome. The training of the neural networks should be obtained with relatively few samples of each written item. The neural network should be capable of detecting subtle differences between written items. The neural network should operate independently of other contextual information.